bioRxivBioengineeringPreprint
All-atom protein design via SE(3) flow matching with ProteinZen
Abstract
The advent of generative models of protein structure has greatly accelerated our ability to perform de novo protein design, especially concerning design at coarser physical scales such as backbone generation and protein binder design. How-ever, the design of precise placements at atomic scales remains a challenge for existing design methods. One avenue towards higher fidelity atomic-scale design is via generative models with full atomic resolution, but is complicated by the intricacies of simultaneously designing both discrete protein sequence and continuous atomic positionings. In this work we propose a framework to capture this interplay by decomposing residues into collections of oriented rigid bodies, allowing us to apply SE(3) flow-matching for all-atom protein structure generation. Our method, ProteinZen1, generates designs with high sequence-structure consistency while retaining competitive diversity and novelty on both unconditional and conditional generation tasks. We demonstrate competitive performance for unconditional monomer design and state-of-the-art performance on various forms of motif scaffolding, including full-atom motif scaffolding and motif scaffolding without specifying motif segment spacing or relative sequence order.
- assay: protein structure
- research focus: protein design
- research focus: sequence-to-function modeling
- approach: deep learning
- approach: diffusion model
- Citations
- 0
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